Genetic mapping of stripe rust resistance in a geographically diverse barley collection and selected biparental populations

Barley stripe or yellow rust (BYR) caused by Puccinia striiformis f. sp. hordei (Psh) is a significant constraint to barley production. The disease is best controlled by genetic resistance, which is considered the most economical and sustainable component of integrated disease management. In this study, we assessed the diversity of resistance to Psh in a panel of international barley genotypes (n = 266) under multiple disease environments (Ecuador, India, and Mexico) using genome-wide association studies (GWASs). Four quantitative trait loci (QTLs) (three on chromosome 1H and one on 7H) associated with resistance to Psh were identified. The QTLs were validated by mapping resistance to Psh in five biparental populations, which detected key genomic regions on chromosomes 1H (populations Pompadour/Zhoungdamei, Pompadour/Zug161, and CI9214/Baudin), 3H (Ricardo/Gus), and 7H (Fumai8/Baronesse). The QTL RpshQ.GWA.1H.1 detected by GWAS and RpshQ.Bau.1H detected using biparental mapping populations co-located were the most consistent and stable across environments and are likely the same resistance region. RpshQ.Bau.1H was saturated using population CI9214/Baudin by enriching the target region, which placed the resistance locus between 7.9 and 8.1 Mbp (flanked by markers sun_B1H_03, 0.7 cM proximal to Rpsh_1H and sun_B1H_KASP_02, 3.2 cM distal on 1HS) in the Morex reference genome v.2. A Kompetitive Allele Specific PCR (KASP) marker sun_B1H_KASP_01 that co-segregated for RpshQ.Bau.1H was developed. The marker was validated on 50 Australian barley cultivars, showing well-defined allelic discrimination and presence in six genotypes (Baudin, Fathom, Flagship, Grout, Sakurastar, and Shepherd). This marker can be used for reliable marker-assisted selection and pyramiding of resistance to Psh and in diversifying the genetic base of resistance to stripe rust.


Introduction
Stripe or yellow rust caused by Puccinia striiformis f. sp.hordei (Psh) is a fungal disease that affects barley production significantly by reducing yield and grain quality.Psh has not yet been detected in Australia and poses a serious exotic pathogen threat, especially considering that Australian barley germplasm has shown a high frequency of susceptibility when tested at the International Maize and Wheat Improvement Centre (CIMMYT) Mexico (Wellings et al., 2000;Derevnina et al., 2015).Genetic resistance is the most cost-effective and sustainable component of integrated disease management.Both qualitative resistance and quantitative resistance to Psh have been reported (Nover and Scholz 1969;Chen and Line 1999;Dracatos et al., 2019), although significantly fewer resistance genes have been formally designated for barley stripe rust than other barley rusts.Conventional resistance to stripe rust in barley is governed by seedling resistance genes that are race specific and have been rendered ineffective in many geographical areas where stripe rust is extant.Very few studies have characterised partial or adult plant resistance (APR) to stripe rust in barley.APR is esteemed for its value in contributing to race non-specific and durable resistance as established in the case of wheat stripe rust.Several recessive (rps) and dominant (Rps) genes (catalogued or provisionally designated) have been identified over the past 40 years (Clare et al., 2016).To date, however, only seven genes have been genetically mapped: rps1 on chromosome 3H (Yan and Chen, 2007); Rps4 on 1H (Johnson, 1968); rps5 on 4H (Esvelt Yan and Chen, 2006;Klos et al., 2016); Rps6 on 7HL (Dawson et al., 2016); Rps7 and Rps8 on 1H and 4H, respectively (Bettgenhaeuser et al., 2021); and Rps9 on 5H (Clare et al., 2016).Many of these genes have been rendered ineffective with the detection of new races of the pathogen.Quantitative trait loci (QTLs) conferring resistance to several formae speciales of P. striiformis were recently identified through genome-wide association studies (GWASs) of barley collections (Vatter et al., 2018;Verma et al., 2018;Visioni et al., 2018) and linkage mapping of biparental populations (Toojinda et al., 2000;Castro et al., 2003;Derevnina et al., 2015).Most of these studies were confined to the identification of QTL without further validation, characterisation, mapping, or development of linked markers, limiting the efficient utilisation of the identified resistance in breeding and marker-assisted selection.
A previous study performed by Singh et al. (2018) assembled an international barley panel of 282 lines (from 26 countries) carrying various levels of field resistance to barley leaf rust (BLR).GWAS on this panel identified 13 QTLs significantly associated with resistance to BLR at adult plant growth stages.We hypothesised that this panel, which carries rich diversity of BLR resistance, may also carry useful stripe rust alleles.This is based on our experience in wheat where partial APR genes have been found to be pleiotropic (effective against multiple pathogens), a theory well exemplified by three wheat APR genes, Lr34/Yr18/Sr57/Pm38, Lr46/Yr29/Sr58, and Lr67/ Yr46/Sr55, conferring resistance to multiple rust pathogens (Park, 2018).
In the present study, we assessed the diversity of stripe rust resistance in a subset of the Singh et al. (2018) international barley panel (n = 266) under three disease environments and performed GWAS to identify genomic regions associated with resistance to Psh populations prevalent in Ecuador, India, and Mexico.In addition, these studies systematically i) validated stripe rust QTL identified via GWAS using five biparental mapping populations, ii) mapped the most stable genomic region on chromosome 1H associated with resistance to stripe rust, and iii) developed closely linked markers for the mapped 1H locus.

Phenotypic evaluation
The barley panel was assessed for BYR at the Instituto Nacional de Investigaciones Agropecuarias (INIAP), Ecuador (2017); CIMMYT, Toluca, Mexico (2019 and2020); and Indian Council of Agricultural Research Centre (ICAR) Flowerdale Research Centre, Shimla, India (2018).The five RIL populations (for mapping) and a set of 50 Australian barley cultivars (for marker validation) were assessed at CIMMYT, Mexico, in a single year (either 2018 or 2020).
At INIAP, Ecuador, the experimental material was sown in blocked groups (1 × 1 m with 30-cm inter-block space).Each block comprised six equally spaced rows (1 m), each representing one test line.Five blocks were sown between and perpendicular to the susceptible spreader rows.The spreaders contained equal parts of the stripe rust susceptible varieties Shyri 89 and Shyri 2000.Spreader rows were infected by naturally occurring Psh inoculum.The field plots at CIMMYT, Mexico, comprised 1-m paired rows sown on top of 0.8-m-wide raised beds.The susceptible spreader variety Apizaco 36 was sown as hill plots in the middle of the 0.5-mwide alleys on one side of each test plot.Greenhouse-increased fresh urediniospores of the Mexican variant of Psh race 24 [PshMEX-1, virulent on stripe rust differentials Topper (no known gene), Cambrinus (Rps4), Mazurka (Rps1.c),Varunda (rpsVa1 and rpsVa2), Emir (rpsEm1 and rpsEm2), Heils Franken (Rps4 and rpsHF), Abed Binder (rps2), and Trumpf (rpsTr1 and rpsTr2), and avirulent on Bigo (Rps1.b)and I 5 (rps3 and rps15) and the bread wheat cultivar Morocco] were suspended in Soltrol 170 oil and sprayed onto ~1-month-old spreaders.At ICAR, India, each panel line was planted as a single 1-m row.To ensure the uniformity of stripe rust infection and maintenance of high disease pressure, a local susceptible line (Barley local) was added as a disease spreader after every 20 lines.Two bordering rows of the susceptible line were sown on all the sides of the panel.Stripe rust inoculations were performed with a mixture of the most predominant and virulent pathotypes just at the emergence of the flag leaf.Fresh urediniospores drawn from a fortnight-old culture were suspended in Soltrol 170 and spray inoculated on spreader rows with the help of atomisers.
Descriptive statistics and histogram visualisation of CI for an international panel at each site and each RIL population were performed using base R (R Core Team, 2020).Correlation coefficients between international panel field sites were performed using the R package "Hmisc" from Harrell (2023).

International panel marker filtering and population structure
The Barley GBS 1.0 platform DArT genotyping identified >13K polymorphic in silico DArT-seq markers for the international panel.The marker data were curated by removing markers that were heterozygous (≥10%), monomorphic, without mapped positions, and with minor allele frequencies (MAFs) <5%.Markers that failed to provide information (i.e., missing data ≥20%) were also removed.Finally, a total of 11,328 unique DArT-seq markers with map positions in the Barley Morex V1 genome assembly (Consortium IBGS, 2012) were selected for further analysis.Chromosome 2H had the highest marker saturation and chromosome 4H had the lowest, with 2,282 and 926 markers, respectively.The number of markers per chromosome is provided in Supplementary File S3, and genome coverage is visualised in Supplementary File S4.Genetic relationships among accessions were investigated using principal component analysis (PCA) performed in R (R Core Team, 2020).A genetic kinship matrix was calculated using the "synbreed" package from Wimmer et al. (2012), and the first three principal components were visualised as a biplot with individuals classified by continent of origin using "ggplot 2" from Wickham (2016).

Genome-wide association mapping
Phenotypic data (BYR CI scores) from four environments were paired with genotypic data (11,328 DArT-seq markers) for GWAS of the international panel.GWAS was performed using a singlelocus mixed linear model with the "rrBLUP" package (Endelman, 2011).Genetic control was investigated based on the quantile versus theoretic quantile (QQ) plots, and five principal components were included as fixed effects in the final model.Kinship relatedness (K) was accounted for in the GWAS linear mixed model through the covariance between lines as calculated with "synbreed".No clustering by class was observed and was therefore not included as fixed in the model.The Manhattan plots derived from the GWAS showed that significant SNP markers had higher −log 10 (p) values than false discovery rate thresholds, suggesting strong marker-trait associations (Supplementary File S4).Significant marker-trait associations were determined using the threshold −log 10 (p) > 4 (significant at the 0.001% level).Marker-trait association was only considered a QTL if −log 10 (p) > 4 significance was detected in at least one environment and two or more markers associated with the trait.Markers positioned within 5 cM on the Barley Morex V1 genome assembly (Consortium IBGS, 2012) were considered part of the same QTL cluster and the most strongly associated marker presented as the QTL "peak".QTLs detected in the international panel follow the naming convention RpshQ.GWA.ChrH.X, where Chr is chromosome, H stands for Hordeum, and X is the identifier.The allele for resistance (phase) was determined for each marker based on the mean effect on phenotype (Supplementary File S5).Linkage disequilibrium (LD) analysis of the DArT-seq markers linked with QTL was performed in R using package snpStats (Clayton, 2023) representing pairwise LD as R 2 between pairs of markers (Supplementary File S6).

Marker frequency analysis of biparental populations
The frequency of the alleles carried by resistant progeny was compared with the frequency of the alleles carried by susceptible progeny in each of the parental encoded RIL sets.A discriminant value reflecting the level of allelic discrimination between the two classes was calculated for each marker (Wenzl et al., 2006(Wenzl et al., , 2007)).A simple chi-squared test was performed at each marker to detect significant discrimination between observed and expected allele frequencies.A differential threshold of >0.1 discriminant value was used to consider a marker significantly associated with a trait, which was calculated to have a <0.3% probability of associating an allele with resistance by chance.Greater than 1 significantly associated marker positioned within 5 cM on the Barley Morex V1 genome assembly (Consortium IBGS, 2012) was considered a QTL and the most strongly associated marker presented as the QTL "peak".The parent contributing to the allele for resistance was determined for each marker (Supplementary File S7).Naming QTL detected in the biparental QTL mapping families follows the convention RpshQ.Donor.ChrH, where Donor is the parental allele genotype and Chr is the chromosome.

RpshQ.Bau.1H region
A 1.46-Mbp (8.14-9.60)genomic region identified on chromosome 1H through GWAS and biparental mapping of CI9214/Baudin RIL population based on the Morex reference v2.0 was enriched with both microsatellite and KASP markers.This genomic region was targeted because it was commonly detected in GWAS and three of the five biparental populations, and additionally, plant defence resistance genes were also identified in this region.
Closely linked DArT-seq markers for the chromosome 1H region harbouring RpshQ.Bau were subjected to BlastN search in the IPK barley blast server (https://galaxy-web.ipk-gatersleben.de/)against barley Morex reference genome v2.0 (2019).Discovered contigs were screened using the Simple Sequence Repeat Identification Tool (SSRIT) program (http://www.gramene.org/gramene/searches/ssrtool), and contigs that included short tandem repeats were used to design 17 simple sequence repeat (SSR) markers using the BatchPrimer3 (https://probes.pw.usda.gov/cgi-bin/batchprimer3/batchprimer3.cgi)program.The 17 SSR primers were tested on CI9214 and Baudin for parental polymorphism using the PCR assay described in Chhetri et al. (2016).The PCR products were separated and visualised on highresolution capillary electrophoresis QIAxcel Advanced System, and gel data were analysed using QIAxcel Screen Gel software.The polymorphic markers were symbolised with the prefix (sun = Sydney University) followed by donor parent and chromosome number.
To develop KASP markers, associated SNPs identified in the target region of RpshQ.Bau.1H were used directly to generate two allele-specific forward primers and one common reverse primer, or vice versa using Batch Primer 3 (https://probes.pw.usda.gov/cgibin/batchprimer3/batchprimer3.cgi).Twenty KASP markers were developed and examined on parental DNA samples including three resistant and susceptible lines from the population using the Bio-Rad CFX96 Touch ™ Real-Time PCR Detection System as described by Chhetri et al. (2016).
Chi-squared analysis was used to verify goodness-of-fit for observed segregation to expected marker genetic ratios.Markers that were polymorphic between resistant and susceptible bulks and parents were mapped in the CI9214/Baudin RIL population to saturate the chromosomal region encompassing RpshQ.Bau.1H.A genetic linkage map was created using QTXb20 software (Manly et al., 2001), and a recombinant fraction (RF) was converted to centimorgan (cM) using the Kosambi map function (Kosambi, 1943).The resulting map spanned 17.1 cM, corresponding to 1.49 Mb in the Barley Morex V2 genome.A logarithm of odds (LOD) score of ≥3 was applied to ascertain the significance of genetic linkages between molecular markers and the resistance locus.MapChart version 2.32 software (Voorrips, 2002) was used for generating the final map.

Disease assessment of the international panel
Stripe rust established well in all four environments (Ecuador, Mexico x2, and India).The disease response (CI) for adult plants assessed across the environments ranged from 0 to 100 (Figure 1).All environments had low-to-moderate positive correlation coefficient for international panel CI: Mexican sites were moderately correlated with each other (r = 0.69***); the Ecuadorian site was moderately correlated with Mexican sites [2019 (r = 0.57***) and 2020 (r = 0.64***)]; the Indian sites had a low correlation with those in Mexico [2019 (r = 0.42***) and 2020 (r = 0.46***)] or Ecuador (r = 0.47***) sites.All correlation coefficients were statistically significant (p < 0.0001), indicating confidence in the correlation presented.At both Mexican sites, CI frequency distribution was skewed towards resistance; at the Ecuadorian site, CI frequency distribution was non-symmetric bimodal, skewed towards resistance, and had a secondary peak at moderate susceptibility; and at the Indian site, CI frequency distribution was "u"-shaped and skewed towards resistance.

Population structure of international panel and linkage disequilibrium
Principal component analysis of genetic similarity was performed on the international panel filtered set of 11,328 DArTseq markers.There was no structured clustering in terms of continents or country of origin observed across the genetic data of the genotypes.The PC1 and PC2 explained the accumulated genotypic variation of 13.34% and 5.40%, respectively (Figure 2).

GWAS of international panel
Analysis of individual stripe rust response data detected a total of four significant QTLs at −log 10 (p) ≥ 4 in at least one environment (Table 1).Markers significantly associated with disease response were identified on chromosomes 1H (three QTLs) and 7H (one QTL).The QTL RpshQ.GWA.1H.1 was detected in two

Biparental marker coverage and mapping of RILs
Populations Pompadour/Zhoungdamei and CI9214/Baudin had the highest marker coverage on chromosome 2H and the lowest on 4H.Marker density in Zug161/Pompadour was the highest on 5H and the lowest on 1H.Marker coverage in Ricardo/Gus was the highest on 7H and the lowest on 4H.Marker diversity in Fumai8/ Baronesse was the highest on 7H and the lowest on 1H.On average, coverage was the highest on 5H (1,286 markers), 2H (1,126 markers), and 7H (1,119 markers) and the lowest on 4H (656 markers) and 1H (700 markers).The number of markers per chromosome in each biparental population is provided in Supplementary File S3.   2).Rpsh_QPom.1Hand Rpsh_QBau.1Hwere detected in the same genomic region in three independent populations, are likely the same, and are hence referred to as RpshQ.Pom/Bau.1H.Closely linked markers for each of these QTLs were identified; their positions and details are presented in Table 2; Supplementary File S7, and Figure 4.

QTL co-location in GWAS and RILs
The QTL RpshQ.GWA.chr1H.1 detected in GWAS of the international panel and the QTL RpshQ.Pom/Bau.1Hcontributed by Pompadour and Baudin (detected in Pompadour/Zhoungdamei, CI9214/Baudin, and Zug161/Pompadour) were the only QTLs that Genomic location of three significant quantitative trait loci (QTLs) from four families (Rpsh_QRic, Rpsh_QPom, Rpsh_QFum, and Rpsh_QBau) and genome-wide association study (GWAS) panel associated with resistance to barley yellow rust and position of peak markers linked to each of the QTL.Scale indicated Mbp on Barley Morex V2 genome assembly (Monat et al., 2019).Thirty-five markers in the vicinity of the RpshQ.Pom/Bau.1Hresistance locus, spanning 8.14 to 9.60 Mbp on chromosome 1H of the Barley Morex V2 genome assembly, were targeted for saturating the genetic map of the CI9214/Baudin population.This region contributed significantly and stably (~35%) to phenotypic variation.We designed 17 SSR markers targeting this interval, with six showing polymorphism.Additionally, 20 KASP markers were designed, with eight being polymorphic.The linkage map, spanning a genetic distance of 17.1 cM and covering 1.49 Mb in the Barley Morex V2 genome, integrated four SSR markers and three KASP markers into the CI9214/Baudin genetic map (Figure 5).KASP marker sun_B1H_KASP_01 co-segregated with RpshQ.Pom/Bau.1H,and SSR markers sun_B1H_03 mapped 0.7 cM distal to RpshQ.Pom/Bau.1H.Marker sun_B1H_KASP_01 was the most robust with clear allelic discrimination.The sequences of these markers are provided in Table 3. Marker sun_B1H_KASP_01 was applied on 50 Australian barley genotypes/cultivars listed in Supplementary File S2.Marker genotyping showed well-defined allelic discrimination for the absence/presence of marker sun_B1H_KASP_01 (Supplementary File S8) and six Australian genotypes (Baudin, Fathom, Flagship, Grout, Sakurastar, and Shepherd) were predicted to carry RpshQ.Pom/Bau.1H.

Discussion
A systematic and efficient breeding approach to developing rustresistant barley cultivars involves the discovery, characterisation, and mapping of new sources of resistance to diversifying the genetic base of resistance and the subsequent reliance on perfectly linked molecular markers for reliable and rapid selection.The studies reported here were conducted to understand the genetic architecture underlying resistance to Psh in a geographically diverse international barley panel, which had been previously mapped for response to leaf rust (Singh et al., 2018).Following the characterisation and mapping of resistance to Psh using extensive phenotyping at three international disease hotspots and GWAS of an international panel, we saturated and validated the underlying major QTL of interest using five biparental mapping populations segregating for stripe rust resistance and developed closely linked PCR-based markers for one of the most consistent and stable loci located on chromosome 1HS.
Our association studies on the international panel detected four QTLs (three on chromosome 1H and one on 7H) associated with resistance to stripe rust across or specific to four environments.Three additional loci were also detected on chromosomes 2H, 3H, and 6H but were associated with only a single marker.Several barley stripe rust GWASs have been conducted over the last 15 years, and over 50 QTLs have been reported across all seven chromosomes.Visioni et al. (2018) detected 15 adult growth-stage QTLs, and only QTL APS_Dg_14_2 on chromosome 2H corresponded with a single marker 2H QTL detected in our study.This region also aligned with QTL QPs.2H-1 detected by Vatter et al. (2018) in a HEB-25 population developed by Maurer et al. (2015).All QTLs detected in our study were distinct from the 25 loci identified in two other studies (Klos et al., 2016;Vatter et al., 2018) likely due to the use of divergent material.
It is not uncommon for GWAS to detect spurious marker-trait associations (false positives) and hence incorrect calling of a QTL (Prins et al., 2016;Kertho et al., 2015).To validate the GWAS results, we performed mapping on five biparental populations and in so doing detected co-location for the resistance loci RpshQ.GWA.Genetic linkage map of chromosome 1H generated from recombinant inbred line (RIL) population CI9214/Baudin (linked marker co-segregating with Rpsh_QBau is highlighted in blue).Scale indicated in centi-Morgan on CI9214/Baudin genetic map.
for anticipatory breeding and pyramiding of resistance genes for achieving durable stripe rust resistance in future cultivars.
FIGURE 2Principal component analysis of the kinship matrix visualising the genetic relationships between 266 lines.The figure on the left (A) represents the first principal component (PC1; x-axis) and the second principal component (PC2; y-axis), and the figure on the right (B) represents PC1 (x-axis) and the third principal component (PC3; y-axis).In both plots, genotypes are coloured according to continent.
RpshQ.GWA.chr1H.3  and RpshQ.GWA.chr7Hweredetectedonly at the Mexico_2019 site.The number of markers contributing to the QTL and peak DArT clone ID are presented in Table1, and −log 10 (p) and effects (ranging from −15.33 to 31.61) are presented in Supplementary File S5.

TABLE 1
QTLs and markers associated with resistance to stripe rust detected under four environments in an association mapping panel (n = 266).